File "D:\pytorch-nested-unet-master\dataset.py", line 63, in getitem img_id + self.mask_ext), cv2.IMREAD_GRAYSCALE)[..., None]) TypeError: 'NoneType' object is not subscriptable

i m getting this error while training model, Can anyone help??

pc:~/unets/pytorch-nested-unet-master$ python train.py --dataset dsb2018_96 --arch NestedUNet Config ----- name: dsb2018_96_NestedUNet_woDS arch: NestedUNet deepsupervision: False dataset: dsb2018_96 input_channels: 3 image_ext: png mask_ext: png aug: False loss: BCEDiceLoss epochs: 10000 early_stop: 20 batch_size: 16 optimizer: Adam lr: 0.0003 momentum: 0.9 weight_decay: 0.0001 nesterov: False

Traceback (most recent call last): File "train.py", line 301, in main() File "train.py", line 222, in main train_test_split(img_paths, mask_paths, test_size=0.2, random_state=41) File "/home/jiyue/anaconda3/envs/judy/lib/python3.7/site-packages/sklearn/model_selection/_split.py", line 2100, in train_test_split default_test_size=0.25) File "/home/jiyue/anaconda3/envs/judy/lib/python3.7/site-packages/sklearn/model_selection/_split.py", line 1782, in _validate_shuffle_split train_size) ValueError: With n_samples=0, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters

File "train.py", line 300, in main() File "train.py", line 206, in main joblib.dump(args, 'models/%s/args.pkl' %args.name) NameError: name 'joblib' is not defined i m getting above error?

<UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation> shows a high IoU of DSB2018 up to 92.37%. This repo has any differences? Or do we need some tricks? have you think about it? Hoping for reply. Thanks

When I use "python train.py --dataset dsb2018_96 --arch NestedUNet" to run the code,why then it turns out"ValueError: With n_samples=0, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters."?

hi, i got a error 'CUDNN_STATUS_EXECUTION_FAILED' when run the test.py. Here is my environment: python3.6, cuda=9.0, cudnn=7.6.5, pytorch=0.4.0. I carefully followed the requirement.txt, but still got this error, any suggestion? Thank you

When I run the train.py,why the results are "loss nan - iou 0.0000 - val_loss nan - val_iou 0.0000"?I run on the dataset dsb2018_96. loss nan - iou 0.0000 - val_loss nan - val_iou 0.0000 Epoch [93/100] 100%|███████████████████████████████████████████████████████████| 33/33 [00:01<00:00, 18.80it/s, loss=nan, iou=5.04e-10] 100%|█████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 36.14it/s, loss=nan, iou=5.76e-10] loss nan - iou 0.0000 - val_loss nan - val_iou 0.0000 Epoch [94/100] 100%|███████████████████████████████████████████████████████████| 33/33 [00:01<00:00, 18.72it/s, loss=nan, iou=5.01e-10] 100%|█████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 36.95it/s, loss=nan, iou=5.76e-10] loss nan - iou 0.0000 - val_loss nan - val_iou 0.0000 Epoch [95/100] 100%|███████████████████████████████████████████████████████████| 33/33 [00:01<00:00, 18.77it/s, loss=nan, iou=5.14e-10] 100%|█████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 36.63it/s, loss=nan, iou=5.76e-10] loss nan - iou 0.0000 - val_loss nan - val_iou 0.0000 Epoch [96/100] 100%|███████████████████████████████████████████████████████████| 33/33 [00:01<00:00, 18.76it/s, loss=nan, iou=5.06e-10] 100%|█████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 36.84it/s, loss=nan, iou=5.76e-10] loss nan - iou 0.0000 - val_loss nan - val_iou 0.0000 Epoch [97/100] 100%|███████████████████████████████████████████████████████████| 33/33 [00:01<00:00, 18.74it/s, loss=nan, iou=5.11e-10] 100%|█████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 37.12it/s, loss=nan, iou=5.76e-10] loss nan - iou 0.0000 - val_loss nan - val_iou 0.0000 Epoch [98/100] 100%|███████████████████████████████████████████████████████████| 33/33 [00:01<00:00, 18.79it/s, loss=nan, iou=5.05e-10] 100%|█████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 36.90it/s, loss=nan, iou=5.76e-10] loss nan - iou 0.0000 - val_loss nan - val_iou 0.0000 Epoch [99/100] 100%|████████████████████████████████████████████████████████████| 33/33 [00:01<00:00, 18.77it/s, loss=nan, iou=5.2e-10] 100%|█████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 37.04it/s, loss=nan, iou=5.76e-10] loss nan - iou 0.0000 - val_loss nan - val_iou 0.0000

100%|██████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.95s/it, loss=0.671, iou=0.63] 100%|██████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.71s/it, loss=0.788, iou=3.97e-10] loss 0.6712 - iou 0.6299 - val_loss 0.7878 - val_iou 0.0000 => early stopping

Mat img = imread("test.png");
float scale = 1.0 / 255;
Size inputSize(256, 256);

Mat inputBolb = cv::dnn::blobFromImage(img, scale, inputSize, Scalar(), true, true);

dnn::Net my_net = cv::dnn::readNetFromONNX("unet_model.onnx");
my_net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
my_net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);

my_net.setInput(inputBolb);
Mat result = my_net.forward();

i try to deploy this model on opencv(after convert pth to onnx, two classes), it seems that result is single channel, so it can only get one label, but label is two classes, so how to get the two classes, any suggestioin?

If the code is not modified, the same output file is created.

https://github.com/4uiiurz1/pytorch-nested-unet/blob/1424f72b7ac3816e450a1c596450e3e68e678f4a/val.py#L105

(output[i, c] * 255).astype('uint8'))

Nice code, thanks for sharing.

As you did not show results, in your opinion where nested-unet/unet++ stands in precision between standard resnet, densenet, squeezenet etc?

Does it perform much better?

Hi, I see in the description the following layout is required for training on a custom dataset:

inputs
└── <dataset name>
    ├── images
    |   ├── 0a7e06.jpg
    │   ├── 0aab0a.jpg
    │   ├── 0b1761.jpg
    │   ├── ...
    |
    └── masks
        ├── 0
        |   ├── 0a7e06.png
        |   ├── 0aab0a.png
        |   ├── 0b1761.png
        |   ├── ...
        |
        └── 1
            ├── 0a7e06.png
            ├── 0aab0a.png
            ├── 0b1761.png
            ├── ...

Let's say 0a7e06.png has one instance of class 0 and no instances of class 1. My question is, is a blank image in the class 1 folder named 0a7e06.png required? Or is 0a7e06.png only required to exist in folder 0?

Thanks

Hello, may I ask which version of albumentations did you use in your code? Now some features of this library have changed, so I do not know which version to use. Thank you very much!

hi everyone I need your help when I run my project I got this error I don't know how to solve ValueError: With n_samples=0, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters. I don't know what kind of error or why this occurs can u please help me Thank you

hi How do I choose type of deep supervision? I can't see this in your code. If I want to use type of ensemble for deep supervision how can I do? pruned too thank you

I read your paper and the original of UNet++ and everything is clear to me apart from how I can I train the network using a new dataset. There is the option of deep supervision in which the nework takes the co-registered image pairs (each has size 256x256x6) concatenated and as output the [output1, output2, output3, output4], with each output dimension of 256x256x1. I would like to reproduce your experiments with deep supervision. It is clear to me what the input should be, but I fail to understand what the output should be. Specifically, how can I produce these 4 output matrices. In the dataset from Lebedev only one grayscale image was used as output, and each pixel of this image was produced from subtracting the two input images.